Training Machine Learning Model

2. The method of claim 1, wherein the machine learning model is a convolutional neural networks (CNN) or a recurrent neural network (RNN).

3. The method of claim 1, wherein the training data is selected from the group consisting of: pathological data; autopilot data; medical experimental data; biological data; internet of things (IoT) data; social network data; e-commerce data.

4. The method of claim 1, wherein the optimizing further comprises minimizing a loss function of the machine learning model.

5. The method of claim 4, wherein the added dynamic noise is selected from a predefined noise set.

6. The method of claim 5, further comprising assigning a corresponding probability to each of the noises according to the loss function, wherein each of the noises is with a different scale from each other.

7. The method of claim 6, wherein the added dynamic noise is selected based on the probability assigned.

8. The method of claim 5, wherein the machine learning model is a CNN, and the predefined noise set comprises noises with three different scales and the training data are labeled pathological images.

9. The method of claim 1, wherein the noise is a Gaussian noise.

11. The system of claim 10, wherein the machine learning model is a convolutional neural networks (CNN) or a recurrent neural network (RNN).

12. The system of claim 10, wherein the training data is selected from the group consisting of: pathological data; autopilot data; medical experimental data; biological data; internet of things (IoT) data; social network data; e-commerce data.

13. The system of claim 10, wherein the optimizing further comprises minimizing a loss function of the machine learning model.

14. The system of claim 13, wherein the added dynamic noise is selected from a predefined noise set.

15. The system of claim 14, further comprising assigning a corresponding probability to each of the noises according to the loss function, wherein each of the noises is with a different scale from each other.

16. The system of claim 15, wherein the added dynamic noise is selected based on the probability assigned.

17. The system of claim 14, wherein the machine learning model is a CNN, and the predefined noise set comprises noises with three different scales and the training data are labeled pathological images.

18. The system of claim 10, wherein the noise is a Gaussian noise.

20. The computer program product of claim 19, wherein the training data is selected from the group consisting of: pathological data; autopilot data; medical experimental data; biological data; internet of things (IoT) data; social network data; e-commerce data.